
# 0.0 ASSESSMENT
# In tending to the evaluation necessities, the culmination of tasks 1, 2, and 3 for the genome NewStrain1 involved a 
#methodical methodology. Task 1 required the arrangement of the genomic dataset by guaranteeing the right designing of the 
#fasta record, setting the establishment for resulting examinations. Task 2 included executing a BLAST analysis utilizing 
#the BLASTp command-line instrument, questioning the arranged dataset against the Yersinia genome database. 
#The assessment of arrangement scores, E-values, and percent characters during manual analysis affirmed the presence of the 
#four genes of premium from the lysis cassette in Tc-PAIY in NewStrain1. Task 3 was achieved by fostering a Python script 
#for succession recovery, empowering the group to separate explicit quality arrangements in view of their ID numbers. 
#Pushing ahead, Task 4 could be accomplished by planning a complete pipeline and manual to direct the group through the 
#reiteration of investigations on an alternate dataset. This includes data planning, BLAST analysis, manual examination of 
#results, and execution of a script for succession recovery, with the manual giving nitty gritty directions to each step. 
#In a social environment, correspondence, joint effort, and a mutual perspective of tasks and objectives are principal. 
#Powerful designation of obligations, clear documentation, and customary advancement refreshes add to a fruitful and strong 
#collective endeavor. The significance of keeping up with open lines of correspondence and regarding different viewpoints 
#encourages a cooperative climate, guaranteeing the group's aggregate achievement.


# 1.O OVERVIEW

# The Workflow 

# The whole workflow is devised to effectively work as a modular bioinformatic pipeline with independent but interrelated 
#parts. Installations, steps, and actions needed to be taken are explained here but it is recommended to consult the NCBI 
#BLAST User Manual and the Anaconda Navigator Manual attached (all credits to respective owners)(NCBI BLAST, 2015). 
# Complete Anaconda-NavigatorTM User Manual attached. Documentation hosted by Read the Docs©. Click the following link to 
#access Read the Docs Anaconda-Navigator Manual ((Anaconda, 2018). Created using Sphinx 5.0.2. Built with the PyData Sphinx 
#Theme 0.14.4.
# Also, find attached complete Anaconda-NavigatorTM Tutorial. Documentation hosted by Read the Docs©. Click the following 
#link to access Read the Docs Anaconda-Navigator Tutorial ((Anaconda, 2018). Created using Sphinx 5.0.2. Built with the 
#PyData Sphinx Theme 0.14.4.
# With working directory attached is an Improv Academic Blueprint-A preliminary literature review integrated as part of 
#modular pipeline which defined this pipeline construction, design and build. Please refer to the document for thorough 
#referential enrichmment of scientific and academic concepts governing this workflow.


# A detailed analysis of BLAST for the Yersinia project was carried out to investigate the genomic features of a 
# newly recognized strain Yersinia. The analysis particularly concentrated on four genes of interest; 
# possibly associated with the phage holin family – a group of proteins involved in bacterial lysis. 
# The main objectives were to determine if these genes characterize the new strain, how their sequences vary, 
# and how they can be compared against sequences from different bacterial strains, thus shedding light on their functional 
# significance and evolutionary implications.


# Holin:
# Holin, a transmembrane protein with 1-4 transmembrane sequences, exhibits diverse classifications based on topological, 
# phylogenetic, and motif analyses, resulting in the identification of 58 families and 7 superfamilies. Experimental evidence 
# from previous cell killing assays indicates that the presence of both the C-terminal region and transmembrane domains is 
# essential for the generation of pores in the inner cell membrane.

# Endolysin:
# Endolysins, enzymatic agents pivotal in bacterial cell lysis, possess a structural composition comprising distinct 
# elements: catalytic domain, cell wall binding domain, linker regions, and accessory domains. Their primary 
# function involves the targeted degradation of the bacterial cell walls peptidoglycan, facilitating the release of phage 
# progeny. Notably, the N-terminal cell wall binding domain recognizes specific bacterial components, while the 
# C-terminal catalytic domain executes enzymatic activity.

# i-spanin and o-spanin:
# Encoded by the RzRz1 genes in both lambda and φ21 phages, i-spanin and o-spanin represent proteins integral to the 
# spanin complex. The proposed model for spanin function elucidates its role in the removal of the outer membrane barrier 
# by fusing it to the inner membrane. Interaction between i-spanin and o-spanin occurs through their C-terminal regions, 
# wherein conformational changes in the two alpha-helix regions of i-spanin facilitate the fusion of the two membranes, 
#ultimately leading to the formation of pores on the bacterial membrane.


#3.0 PROJECT PLANNING
#To smooth out the analysis interaction and empower the group to rehash the examinations on an alternate dataset, 
#a pipeline alongside a complete manual can be given. Here is an improved version of pipeline:

#Pipeline:
#1.	Step 1: Data Preparation
#•	Input: Fasta file of the new Yersinia strain genome.
#•	Output: Prepared dataset.
#2.	Step 2: BLAST Analysis
#•	Input: Prepared dataset and known protein sequences.
#3•	Output: BLAST results file.
#3.	Step 3: BLAST Results Analysis
#•	Input: BLAST results file.
#•	Output: Confirmation of gene presence.
#4.	Step 4: Script Execution
#•	Input: Fasta file, target gene ID.
#•	Output: Retrieved gene sequence.

#Manual
#•	Detailed instructions for each step.
#•	Necessary parameters and commands for tools/scripts.
#•	Troubleshooting guidance.

#Explanation of Findings
#•	Clarification of the significance of alignment scores, E-values, and percent identities.
#•	Confirmation or rejection of the presence of genes based on the analysis.
#•	Interpretation of any unexpected results or discrepancies.


#Import Required Libraries
from Bio import SeqIO
from Bio.Blast import NCBIWWW
import os


from Bio import Entrez

# Set your email here
Entrez.email = "your_email@example.com"

# List of provided protein IDs
ids = [
    "CAI77377.1",
    "AAT90758.1",
    "WP_004875774.1",
    "WP_050116509.1",
    "AAM83780.1",
    "ABX88260.1",
    "CAH22792.1",
    "CAE13263.1",
    "AAG55972.1",
    "WP_047345313.1",
    "WP_047964150.1",
    "AAQ61208.1"
]

# Open a file to write the sequences
# Running the Script

    #Place the FASTA File: Ensure the holin_sequences.fasta file is in the same directory as the script.
    #Open Command Line: Use Terminal (Mac/Linux) or Command Prompt (Windows).
    #Navigate to Script Directory: Change directory to where the script is located using cd path/to/script.
    #Run the Script: Execute the script by typing python script_name.py (replace script_name.py with the actual script filename).
    
with open("holin_sequences.fasta", "w") as file:
    for i in ids:
        # Fetch each sequence from NCBI and write to the file
        handle = Entrez.efetch(db="protein", id=i, rettype="fasta", retmode="text")
        file.write(handle.read())


3. Be able to retrieve their specific protein sequence from the results (or any other in the future)

#The research group imagines a deliberate pipeline intended to investigate extra genes of premium inside the genomic scene 
#of the new Yersinia strain, NewStrain1. The initial step includes setting up the genomic dataset, guaranteeing appropriate 
#designing to work with downstream investigations. Consequently, the group utilizes the BLASTp command-line instrument to 
#direct an underlying analysis against an exhaustive quality database, producing a rundown of potential genes of interest. 
#To refine and focus on this rundown, a separating cycle is carried out, taking into account alignment scores, E-values, 
#and #other significant models. The third step coordinates practical explanation tools, like InterProScan, to clarify the 
#potential jobs and elements of the recognized up-and-comer genes. The refined rundown is additionally approved through a 
#complete writing survey or computerized tools for writing mining, guaranteeing that the chose genes have known capabilities 
#or affiliations. As a closing step, a discretionary arrangement for experimental approval is incorporated, considering 
#further affirmation of the distinguished genes through research center investigations or joint efforts with experimental 
#biologists. This comprehensive pipeline offers an organized and precise methodology for the group to investigate, 
#distinguish, and examine extra genes of premium in NewStrain1's genomic setting.


#Function to Retrieve Sequences from a FASTA File

def retrieve_sequence(fasta_file, gene_id):
    for record in SeqIO.parse(fasta_file, "fasta"):
        if record.id == gene_id:
            return record.seq
    return None


A. #Function to Perform BLAST Analysis

def blast_sequence(sequence, database="nr", program="blastp", format_type="Text"):
    result_handle = NCBIWWW.qblast(program, database, sequence, format_type=format_type)
    return result_handle.read()


# Main Workflow
def main():
    # Define the path to your FASTA file and gene IDs
    fasta_file = "holin_sequences.fasta"
    gene_ids = [
    "CAI77377.1",
    "AAT90758.1",
    "WP_004875774.1",
    "WP_050116509.1",
    "AAM83780.1",
    "ABX88260.1",
    "CAH22792.1",
    "CAE13263.1",
    "AAG55972.1",
    "WP_047345313.1",
    "WP_047964150.1",
    "AAQ61208.1"
]
    
    # Loop through each gene ID
    for gene_id in gene_ids:
        sequence = retrieve_sequence(fasta_file, gene_id)
        if sequence:
            print(f"Sequence for {gene_id}: {sequence}")
            # Perform BLAST analysis
            blast_result = blast_sequence(sequence)
            # Save BLAST results to a file
            with open(f"{gene_id}_blast_results.txt", "w") as result_file:
                result_file.write(blast_result)
            print(f"BLAST results for {gene_id} saved.")
        else:
            print(f"Sequence for {gene_id} not found in {fasta_file}")

if __name__ == "__main__":
    main()


# Output file for consolidated results
fasta_file = "holin_sequences.fasta"
gene_ids = [
    "CAI77377.1",
    "AAT90758.1",
    "WP_004875774.1",
    "WP_050116509.1",
    "AAM83780.1",
    "ABX88260.1",
    "CAH22792.1",
    "CAE13263.1",
    "AAG55972.1",
    "WP_047345313.1",
    "WP_047964150.1",
    "AAQ61208.1"
]
with open("consolidated_blast_results.txt", "w") as consolidated_file:
    # Loop through each gene ID
    for gene_id in gene_ids:
        sequence = retrieve_sequence(fasta_file, gene_id)
        if sequence:
            # Write sequence to the consolidated file
            consolidated_file.write(f"Sequence for {gene_id}:\n{sequence}\n")
            
            # Perform BLAST analysis
            blast_result = blast_sequence(sequence)
            
            # Save BLAST results to individual files
            with open(f"{gene_id}_blast_results.txt", "w") as result_file:
                result_file.write(blast_result)
            
            # Write BLAST results to the consolidated file
            consolidated_file.write(f"BLAST results for {gene_id}:\n{blast_result}\n")
            
            print(f"BLAST results for {gene_id} saved.")
        else:
            print(f"Sequence for {gene_id} not found in {fasta_file}")


# 4.0 IMPROMPTU LITERATURE REVIEW 

#The analysis identified the presence of homologous genes to phage holin family proteins, notably through proteins 
#“WP_005163187.1” and “MBO0362545.1”. These proteins had high alignment scores and low E-values in the BLAST results, 
#meaning that they closely matched the genes of interest in this new strain of Yersinia. This visionary evidence also 
#indicates that these phage holin-related genes have been incorporated into the genome of the new strain, which might bear 
#their influence on the biological characteristics of this new strain such as its pathogenicity.


#5.0 FURTHER VISUALISATION AND ANALYSIS
#4. To understand the different outputs from BLAST and how to analyse them

#In the situation where the research group tries to fathom the different results created by BLAST (Basic Local Alignment 
#Search Apparatus) and foster a comprehension of how to break down them, a precise methodology is framed.

#1.1 Perform BLAST Analysis
#To start the cycle, the group chooses the DNA or protein successions of interest, explicitly those beginning from the 
#four genes inside the lysis cassette in Tc-PAIY, to act as queries in the BLAST analysis. The decision of the BLAST 
#apparatus is dependent upon the idea of the arrangements, whether they are nucleotide or protein, and sticks to explicit 
#database necessities.


#1.2 Examine BLAST Output
#Upon execution, the BLAST device produces a result document outfitting alignments between the question arrangements and 
#those present in the chose database. The resultant data normally incorporates a thorough rundown of matches, including 
#alignment scores, E-values meaning statistical importance, and other relevant data fundamental for additional analysis.

#1.3 Interpret Alignment Scores
#In this situation, the research group digs into the assessment of alignment scores. Higher scores are proposed to 
#connote more grounded likenesses between the inquiry successions and the hits inside the database, giving a 
#quantitative proportion of alignment quality.

#1.4 Evaluate E-values
#The statistical meaning of the alignments is surveyed through E-values, addressing the expected number of irregular 
#hits having a comparative or unrivaled score. Lower E-values are focused on, demonstrating higher trust in the noticed 
#similitudes.

#1.5 Assess Sequence Identities and Similarities
#The group examines the level of character and similitude inside the BLAST results. These measurements explain the level of 
#resemblance between the question successions and the matched database groupings, assuming a significant part in grasping 
#grouping preservation.

#Visualize Alignments
#To upgrade understanding, the group might decide to utilize perception tools or programming, working with the graphical 
#portrayal of alignments. This perception supports clarifying explicit locales of comparability and disparity inside the 
#successions.

#Filter and Refine Results
#Contingent upon the targets of the research, the group takes part in the filtration and refinement of BLAST results in 
#light of foreordained standards. Boundaries like alignment length, percent character, or E-esteem limits are potentially 
#utilized to distil the most appropriate data.

#Compare Multiple Hits
#In getting different hits, the research group embraces a near analysis. Different boundaries are considered to focus on 
#and select the most pertinent matches among the plenty of results.

#Document and Report Findings
#At long last, the group participates in fastidious documentation and announcing of their discoveries. This complete record 
#incorporates chosen hits, nitty gritty alignments, and relevant measurements, guaranteeing straightforwardness and giving 
#a primary asset to future reference and analysis.

STUDENT NUMBER	2502495
Programme	MSc Bioinformatics
Unit Code and Name	BIOLMM37_2023_TB-1 Group Project
Project	2
Assessmment Name	Group Project Alternative Assessment
Word Count	Well under 1500 words
Do you give permission for you work to be used anonymously in examples given to students in the future?	Yes
Title of the Report	PROJECT 2: PATHOGENIC ISLAND DETECTION IN A NEW STRAIN OF YERSINIA
Essential UoB Literature Referenced	Workbooks on Accelerated R Couse by Dr Christopher Clements, Statistics Bootcamp, Statistics for Biological Sciences, Year 2 - Quantitative and Computational Methods - 2020 iteration by Multiple Authors and Year 4 - Understanding Data: Experimental Design and Statistics for Life Scientists - 2020 iteration by Multiple Authors on Blackboard (Dr. Christopher Clements, Duncan O’Brien, Duncan Edgley, et al), Genomics Bootcamp by Dr Francesca Segers on Blackboard, Command Line Working Document, and Project Description Powerpoint, by Dr Celine Petitjean on Blackboard
Unit Director	Dr Celine Petitjean
Date of Submission	Monday, January 22, 2024
Place	Life Science Building (LSB), School of Biological Sciences, Faculty of Life Sciences, University of Bristol
Marks based on the summmative computer based assessmment commbininng all the leaarning objectives	100%.

PROJECT 2: PATHOGENIC ISLAND DETECTION IN A NEW STRAIN OF YERSINIA¶

Quintessentially Paradigmatic Plan, Designed Based on Knowledge of Various Biological Concepts, Developed and Implemented Different Strategies to Tackle a Scientific Problem Taking Advantage of the Modularity of Bioinformatics Approaches.¶

Accessible via my GitHub Repository https://github.com/2502495/Project-2.git.¶

The format of this Scientific Report is taken from the Skills Team, University of Hull available on University of Bristol¶

BlackBoard® Day, R.A. (1998), and Teaching and Learning Support (TaLS) – Fact Sheets from University of Bristol Blackboard®.¶

5.0 RESULTS¶

BLAST Results Deciphering the Match Between Query vs the Database¶

The Basic Local Alignment Search Tool (BLAST) offers a powerful means to assess the relationship between a query sequence¶

and a vast protein database. By analyzing the alignment details, researchers can gain valuable insights into the degree of¶

similarity, evolutionary history, and functional potential of the queried sequence. Several key parameters within the¶

BLAST results facilitate this interpretation:¶

Sequence Similarity¶

1. Percentage Identity: This metric quantifies the percentage of identical residues between the query and subject sequences¶

within the aligned region. Higher values indicate a closer relationship, suggesting potential functional conservation or¶

shared ancestry.¶

2. Alignment Length: This parameter represents the total number of residues included in the alignment, encompassing both¶

matches and gaps. A longer alignment signifies a more extensive region of similarity, potentially hinting at a shared¶

functional domain or structural motif.¶

Alignment Features:¶

3. Mismatches: This metric indicates the number of positions within the aligned region where the query and subject sequences #differ. While mismatches can arise due to mutations, their abundance can also point towards functional divergence or #convergent evolution.¶

4. Gap Opens: This parameter denotes the number of gaps introduced within the alignment to accommodate insertions or¶

deletions in either the query or subject sequence. Frequent gaps can suggest lineage-specific adaptations or insertions of¶

functional elements.¶

Positional Information:¶

5. Query Start/End & Subject Start/End: These parameters define the precise locations within the query and subject¶

sequences where the aligned region begins and terminates. This information is crucial for mapping functional domains or¶

conserved motifs identified within the alignment back to their respective sequences.¶

Statistical Significance:¶

6. E-Value: (Expect Value) This parameter estimates the number of alignments with equal or better scores that are expected¶

to occur by chance in a random sequence database. A lower E-Value indicates a more statistically significant match,¶

suggesting a less likely occurrence by chance.¶

Bit Score: This statistical score, calculated by the BLAST algorithm, reflects the quality and strength of the alignment.¶

A higher bit score signifies a more robust and informative alignment, suggesting a closer evolutionary relationship or¶

functional similarity between the query and subject sequences.¶

By carefully analyzing these parameters in conjunction, researchers can decipher the intricate relationship between the¶

queried amino acid sequence and its potential matches within the protein database. This detailed understanding facilitates¶

hypothesis generation, functional prediction, and ultimately, advances our knowledge of protein evolution and function.¶

Execution of the following Python script will perform data analysis and visualisation¶

The appearance of similar sequences in various Yersinia species points to a strong genetic relationship between the¶

phage holin family genes and this genus. In addition to confirming the genetic relationship between Yersinia species,¶

this finding may point towards an evolutionary path of these genes in the genus. The high representation of phage holin¶

family proteins in the hits lends support to the putative hypothesis that genes of interest identified from this new¶

Yersinia strain may well operate through a similar mechanism or bear close homology to such protein families.¶

Further Data Analysis and Future Research Potential¶

The BLAST results suggest the potential pathogenicity of NewYersinial, with discernible disparities in amino acid¶

sequence relative to W22703, potentially influencing its structural conformation and, consequently, functional¶

distinctions. In order to delineate the intricacies of structural and sequential variations across all target genes¶

between NewYersinia1 and W22703, SWISS model was employed for protein structure simulations. The resulting models,¶

exhibited in rainbow mode with blue denoting N-terminus and red indicating C-terminus, enabled a meticulous analysis.¶

Holin¶

Hypothesis:¶

Sequence variations in NewYersinia1 holin, despite being located in transmembrane domains (alpha helices),¶

may not significantly alter its overall structure, potentially leading to functional divergence from W22703.¶

Methods:¶

SWISS-model was employed to generate simulated protein structures of NewYersinia1 and W22703 holins.¶

Sequence alignment was performed to identify specific variations between the two proteins¶

Results:¶

Simulated models revealed that both holins possess similar overall structures, despite the presence of few sequence¶

variations within the transmembrane domains.¶

Discussion:¶

The observed sequence variations, while located in functionally important regions, might not significantly impact the¶

global protein fold of NewYersinial holin.¶

This suggests that, while functional efficiency may differ between NewYersinia1 and W22703 holins, their core holin¶

function is likely preserved.¶

Further research:¶

Functional assays are necessary to definitively determine the impact of sequence variations on NewYersinial holin¶

activity and its role in potential pathogenicity.¶

Detailed structural analyses, potentially through mutagenesis and experimental validation, could provide deeper¶

insights into the specific effects of these variations on protein-protein interactions and membrane dynamics.¶

Key takeaways:¶

Sequence variations in NewYersinial holin, despite their potential functional implications, do not appear to¶

drastically alter its overall structure compared to W22703.¶

Further investigation is crucial to elucidate the precise functional consequences of these variations and their¶

contribution to NewYersinial's potential pathogenic behavior.¶

Endolysin¶

Hypothesis:¶

Despite the presence of sequence variations, NewYersinia1 endolysin might exhibit a conserved structure and function¶

compared to W22703 due to the minimal nature of the variations and their location outside of critical functional domains.¶

Methods:¶

Sequence alignment was performed to identify specific variations between NewYersinia1 and W22703 endolysins.¶